Package: TDAstats 0.4.1
TDAstats: Pipeline for Topological Data Analysis
A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) <doi:10.21105/joss.00860>. For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) <doi:10.1140/epjds/s13688-017-0109-5>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>. To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at <https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.
Authors:
TDAstats_0.4.1.tar.gz
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TDAstats.pdf |TDAstats.html✨
TDAstats/json (API)
# Install 'TDAstats' in R: |
install.packages('TDAstats', repos = c('https://rrrlw.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rrrlw/tdastats/issues
data-scienceggplot2homologyhomology-calculationshomology-computationjosspersistent-homologypipelineripsertdatopological-data-analysistopologytopology-visualizationvisualization
Last updated 3 years agofrom:777d70f6fc. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 06 2024 |
R-4.5-win-x86_64 | NOTE | Nov 06 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 06 2024 |
R-4.4-win-x86_64 | NOTE | Nov 06 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 06 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 06 2024 |
R-4.3-win-x86_64 | NOTE | Nov 06 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 06 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 06 2024 |
Exports:calculate_homologyid_significantpermutation_testphom.distplot_barcodeplot_persist
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr
Data Input for TDAstats
Rendered frominputformat.Rmd
usingknitr::rmarkdown
on Nov 06 2024.Last update: 2018-08-08
Started: 2018-08-08
Hypothesis testing with TDAstats
Rendered frominference.Rmd
usingknitr::rmarkdown
on Nov 06 2024.Last update: 2018-07-27
Started: 2018-07-26
Introduction to persistent homology with TDAstats
Rendered fromintro.Rmd
usingknitr::rmarkdown
on Nov 06 2024.Last update: 2019-12-12
Started: 2018-07-26
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Calculate Persistent Homology of a Point Cloud | calculate_homology |
2-dimensional point cloud of a unit circle | circle2d |
Identify Significant Features in Persistent Homology | id_significant |
Statistical Inference for Topological Data Analysis | permutation_test |
Calculate Distance between Homology Matrices | phom.dist |
Plot Persistent Homology as Topological Barcode | plot_barcode |
Plot Persistent Homology as Persistence Diagram | plot_persist |
3-dimensional point cloud of a unit sphere | sphere3d |
Statistical Inference for Persistent Homology in Topological Data Analysis | TDAstats-package TDAstats |
2-dimensional point cloud of a unit square | unif2d |
3-dimensional point cloud of a unit cube | unif3d |